Reducing Rivals Prices: Government-Supported Mavericks as New solutions for Oligopoly Pricing
Bibliographic record
Abstract
One of the most important market imperfections in modern capitalism, and surprisingly one of the most under-regulated, is oligopoly pricing (conscious parallelism). Only a few suggestions have been made over the years to regulate oligopoly pricing, and all of them pose serious obstacles to their efficient application. Consequently, oligopoly pricing is left to the workings of the market (or pure luck), even though the market's limited regulatory role is acknowledged. This article proposes a novel method for regulating oligopoly pricing by way of introducing a governmentsupported maverick into an oligopolistic industry for a limited time. The maverick will price its products at competitive or near-competitive levels, based on considerations of consumer or total welfare. Its rivals will follow its pricing strategy or incur significant losses, and possibly exit the market. As will be shown, the proposal may significantly reduce allocative inefficiency by reducing the welfare losses from supra-competitive pricing. The threat of intervention might be sufficient, in itself, to reduce the problem of oligopoly pricing. It may also reduce productive inefficiency by combatting the problem of inefficient plant and firm sizes. This article analyzes the market conditions that must exist for this proposal to be operational and indicates its benefits as well as its costs and limitations. More subtle versions of the model, such as granting tax exemptions to new entrants and reducing transportation costs into the market, may also potentially reduce oligopoly pricing. * Assistant Professor, Haifa Univeristy School of Law and Academic Fellow, NYU Center for Law and Business, NYU Law School and Stern School of Business. LL.B. (Tel Aviv University) LL.M., S.J.D. (University of Toronto). The author wishes to thank Bill Allen, Jennifer Arlen, Ian Ayres, Dafna Barak-Erez, Jean Pierre Benoit, Margaret Bloom, Rob Daines, Aaron Edlin, Alan Fels, Victor Goldberg, Marcel Kahan, Ehud Kamar, Menni Mautner, Geoff Miller, Januz Ordover, Ariel Porat, Steven Salop, Michael Trebilcock and Omri Yadlin for helpful comments on previous drafts or helpful discussions. All errors and omissions remain the author's. HeinOnline -7 Stan. J.L. Bus. & Fin. 73 2001-2002 Stanford Journal of Law, Business, & Finance
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".